Papers with sequential generation
Towards Answer-unaware Conversational Question Generation (D19-58)
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| Challenge: | Existing frameworks for conversational question generation are answeraware, but are not able to generate corresponding answers . a number of question generation methods are developed for text-based question answering . |
| Approach: | They propose a framework for conversational question generation that is unaware of the corresponding answers. |
| Outcome: | The proposed framework is effective but answeraware, the authors show . it improves quality of generated questions if question foci and question patterns are identified . |
Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)
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| Challenge: | Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning . |
| Approach: | They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions. |
| Outcome: | The proposed model can assess the flawed summary from a global view and modify inappropriate expressions. |
NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation (2023.acl-long)
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Shengming Yin, Chenfei Wu, Huan Yang, Jianfeng Wang, Xiaodong Wang, Minheng Ni, Zhengyuan Yang, Linjie Li, Shuguang Liu, Fan Yang, Jianlong Fu, Ming Gong, Lijuan Wang, Zicheng Liu, Houqiang Li, Nan Duan
| Challenge: | Existing work generates long videos segment by segment sequentially, which is inefficient. |
| Approach: | They propose a Diffusion over Difference architecture for eXtremely Long video generation. |
| Outcome: | The proposed architecture reduces the average inference time from 7.55min to 26s (94.26%) and generates high-quality long videos with both global and local coherence. |
BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination (2026.acl-long)
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| Challenge: | Existing lists of document ranking methods lack robust performance across domains. |
| Approach: | They propose a reasoning-driven competitive elimination framework that optimises group sizes based on LLM context limits and reasoning-enhanced prompts. |
| Outcome: | The proposed method outperforms RankGPT and other state-of-the-art methods on datasets with a 77.90 NDCG@5 score and 54.66 average NDGC@10 on BEIR datasets. |
GoT-R1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning (2026.findings-acl)
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| Challenge: | Chain-of-Thought reasoning suffers from an inherent mechanism flaw: linearity induces overthinking . emergence of Large Language Models (LLMs) has fundamentally redefined artificial intelligence . |
| Approach: | They propose a framework that replaces verbose linear trajectories with high-density reasoning graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art models with reduced token overhead. |
Tree-Notebook: A Context-Aware Agent with Tree Search and Entropy-Aware Data Shadow for Interactive Data Science (2026.findings-acl)
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| Challenge: | Experimental results show that Tree-Notebook achieves state-of-the-art (SOTA) performance on InfiAgent-DABench and DSBench. |
| Approach: | They propose an agentic framework that mimics the iterative cognitive process of human data scientists. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on InfiAgent-DABench and DSBench. |
Visual Self-Refinement for Autoregressive Models (2025.findings-emnlp)
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Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao
| Challenge: | Autoregressive models excel in sequential modeling but the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. |
| Approach: | They propose a plug-and-play refinement module to enhance the spatial correspondence modeling within the generated visual sequence. |
| Outcome: | The proposed module enhances vision-language modeling under a shared sequential prediction framework. |